Search Results (31)

This systematic review explores the role of smartwatches in stress management, mental health monitoring, and overall well-being. Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and user-centered design evaluations. Smartwatches, equipped with sensors for physiological signals such as heart rate, heart rate variability, electrodermal activity, and skin temperature, have demonstrated promise in detecting and predicting stress and mood fluctuations in both clinical and everyday contexts. This review emphasizes the need for interdisciplinary collaboration to advance technological precision, ethical data handling, and user experience design. Moreover, it highlights how different algorithms—such as Support Vector Machines (SVMs), Random Forests, Deep Neural Networks, and Boosting methods—perform across various physiological signals (e.g., HRV, EDA, skin temperature). Furthermore, it identifies performance trends and challenges across lab-based vs. real-world deployments, emphasizing the trade-off between generalizability and personalization in model design. Full article

Cacao (Theobroma cacao) is considered a functional food due to its composition, which is rich in bioactive compounds such as flavonoids, theobromine, dietary fiber, and essential minerals. Several studies have shown that flavonoids have antioxidant and anti-inflammatory properties, helping to reduce oxidative stress and protecting against cardiovascular diseases. In addition, their ability to stimulate nitric oxide production improves blood circulation and lowers blood pressure. These benefits, coupled with its ability to improve mood and cognitive function, position cocoa as a key ingredient in the development of functional foods aimed at improving quality of life and preventing chronic diseases. This research aims to create a product that incorporates cocoa and essential oils extracted from aromatic plants native to the department of Cauca. This represents a significant step toward the sustainable use of these ingredients in the region, promoting consumer welfare and health while strengthening sustainable practices, fostering innovation, and boosting economic and social development in the department. The research is developed in five phases: determination of the study area, characterization of the cocoa production chain in the department of Cauca, selection of essential oils, application of an experimental mixture design and physicochemical and microbiological analyses of the final product. From the experimental design of the mixture, it was determined that the most appropriate formulation of the bar is 60% dark chocolate (70% cocoa), 29% sweet chocolate, 10% pure strawberry and 1% fennel essential oil (Foeniculum vulgare), reaching an average sensory acceptability of 3.23 on a five-point hedonic scale. The qualitative properties (organoleptic, chemical and microbial) of the selected formulations are acceptable for human consumption and provide a high energy content of 506.25 kcal/100 g for chocolate bars filled with strawberry puree and fennel essential oil. Full article

Background/Objectives: A mango (Mangifera indica) leaf extract (Zynamite^®), rich in the polyphenol mangiferin, has been demonstrated to modulate brain activity, boost cognitive function, and reduce mental fatigue. Research evidence supports that improving the solubility of this extract could significantly enhance its efficacy as an active ingredient. This study examined the effects of a soluble version of Zynamite^®, Zynamite^® S (Zyn-S), on cognitive function and mood in young adults at low doses. Methods: A total of 119 university students were enrolled in the study. Participants were randomly assigned to receive either 100 mg, 150 mg, or placebo in a double-blind crossover design. Short- and long-term memory were evaluated using the Rey Auditory Verbal Learning Test (RAVLT), executive functions with the Trail Making Test (TMT), processing speed with the Digit Symbol Substitution Test (DSST), and selective attention with the Stroop Color and Word Test. Additionally, mood was assessed using the Spanish short version of the Profile of Mood States (POMS). All these assessments were conducted before taking the product and at 30 min, 3 h, and 5 h post-intake. Results: The results demonstrated that participants who received Zynamite^® S experienced significant improvements in reduced tension, depression, and confusion, suggesting an enhancement in mental clarity and overall emotional well-being. Both interventions also improved processing speed and cognitive flexibility. However, no significant differences were observed in short- and long-term verbal memory. Conclusions: In summary, these findings support Zynamite^® S as a natural nootropic capable of acutely improving key cognitive functions and emotional balance at low doses in young adults, with sustained efficacy for at least five hours. Full article

Functional beverages comprise a special category of drinks free of alcohol that contain bioactive components from plant, animal, marine, or microorganism sources that contribute to the reinforcement of human health. Functional beverages are mainly divided into the following basic categories: (i) dairy-based beverages and (ii) non-dairy-based beverages. Functional beverages have several positive functional properties such as the rehydration of the body, recovery of lost energy, the increase of athletic performance, the prevention of pain in joints, the improvement of heart health, the improvement of immunity and the digestive system, and the creation of the feeling of satiety and boosting mood. However, according to health experts, there are also functional beverages that induce obesity and heart diseases because of their high content of sugars, sweeteners, and other components such as caffeine, taurine, taurine combined with caffeine, creatinine, etc. The scope of this review was to highlight the main components and the functional properties of energy drinks along with the effects of functional beverages on human health. Limited review articles address this overall hypothesis in the recent literature, thus comprising the significance of the current study. Full article

There are many influences on the piano playing learning process. Research on combining it with self-regulated learning (SRL) methods has rarely been reported. This study aimed to elucidate the complex relationships between social skills, anxiety, and self-efficacy (SE) in piano learning and performance contexts. The question of whether reflection enhances SRL effectiveness was also raised. The participants included 24 Chinese piano students, who were divided into three groups and received different emotional interventions. Over the course of an eight-week study program, the groups were exposed to different teaching methods. One group received positive emotional input designed to boost confidence, another group encountered negative emotional input that triggered stress, and a control group received a routine without emotional manipulation. The relationship between anxiety, self-efficacy, social skills, mood, and reflection were explained through quantitative academic performance results and qualitative return interviews. The results showed that self-efficacy was significantly negatively related to anxiety levels, while transient emotional states had minimal impact on immediate learning outcomes. Reflection, on the other hand, led to the increased effectiveness of SRL, which could quickly alleviate anxiety and increase self-efficacy by enhancing students’ reflective process after class. This study also highlights the complexity of the interplay between social skills and self-efficacy, as well as anxiety levels. These findings suggest that fostering self-efficacy and reflective practices in piano education can help manage student anxiety and improve learning outcomes, providing valuable insights for teaching strategies. Full article

Menopause leads to a decline in estrogen levels, resulting in significant metabolic alterations that increase the risk of developing metabolic syndrome—a cluster of conditions including central obesity, insulin resistance, dyslipidemia, and hypertension. Traditional interventions such as hormone replacement therapy carry potential adverse effects, and lifestyle modifications alone may not suffice for all women. This review explores the potential role of palmitoylethanolamide (PEA), an endogenous fatty acid amide, in managing metabolic syndrome during the postmenopausal period. PEA primarily acts by activating peroxisome proliferator-activated receptor-alpha (PPAR-α), influencing lipid metabolism, energy homeostasis, and inflammation. Evidence indicates that PEA may promote the browning of white adipocytes, enhancing energy expenditure and reducing adiposity. It also improves lipid profiles by boosting fatty acid oxidation and decreasing lipid synthesis, potentially lowering low-density lipoprotein cholesterol and triglyceride levels while increasing high-density lipoprotein cholesterol. Additionally, the anti-inflammatory properties of PEA enhance insulin sensitivity by reducing pro-inflammatory cytokines that interfere with insulin signaling. PEA may aid in weight management by influencing appetite regulation and improving leptin sensitivity. Furthermore, its neuroprotective effects may address the mood disturbances and cognitive decline associated with menopause. Given these multifaceted biological activities and a favorable safety profile, PEA may represent a promising non-pharmacological supplement for managing metabolic syndrome in postmenopausal women. However, further large-scale clinical studies are necessary to establish its efficacy, optimal dosing, and long-term safety. If validated, PEA could become an integral part of strategies to improve metabolic and neuropsychological health outcomes in this population. Full article

Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep’s impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming. To address this challenge, automated brain monitoring has emerged as a crucial solution for cost-effective and efficient EEG data analysis. A critical component of sleep analysis is detecting transitions between wakefulness and sleep states. These transitions offer valuable insights into sleep quality and quantity, essential for diagnosing sleep disorders, designing effective interventions, enhancing overall health and well-being, and studying sleep’s effects on cognitive function, mood, and physical performance. This study presents a novel EEG feature extraction pipeline for the accurate classification of various wake and sleep stages. We propose a noise-robust model-based Kalman filtering (KF) approach to track changes in a time-varying auto-regressive model (TVAR) applied to EEG data during different wake and sleep stages. Our approach involves extracting features, including instantaneous frequency and instantaneous power from EEG, and implementing a two-step classifier for sleep staging. The first step classifies data into wake, REM, and non-REM categories, while the second step further classifies non-REM data into N1, N2, and N3 stages. Evaluation on the extended Sleep-EDF dataset (Sleep-EDFx), with 153 EEG recordings from 78 subjects, demonstrated compelling results with classifiers including Logistic Regression, Support Vector Machines, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The best performance was achieved with the LGBM and XGBoost classifiers, yielding an overall accuracy of over 77%, a macro-averaged F1 score of 0.69, and a Cohen’s kappa of 0.68, highlighting the efficacy of the proposed method with a remarkably compact and interpretable feature set. Full article

Women’s knowledge about perimenopause plays a crucial role in shaping their perception of related illnesses, influencing coping strategies, treatment adherence, and the overall management of this life stage. This cross-sectional study assessed the awareness, knowledge, attitudes, and practices regarding perimenopause among 409 Saudi women attending primary healthcare centers. Participants completed a structured questionnaire addressing demographic data, awareness, knowledge, attitudes, and practices related to perimenopause. While 75.3% of the participants were aware of perimenopause, only 17.4% could identify more than 10 out of 20 perimenopause symptoms. Commonly recognized symptoms included menstrual irregularity (67.7%), mood swings (66.0%), and mood fluctuations (50.4%). Only 23.0% had optimal knowledge about perimenopause complications. Additionally, 73.3% had not consulted a doctor for perimenopause-related issues. An analysis of the overall knowledge score showed a mean (SD) = 14.82 (5.64) out of 34. The level of knowledge was independently associated with a higher educational level, more frequent perimenopause symptoms, and regular doctor visits. This study reveals high awareness but insufficient knowledge among Saudi women regarding perimenopause symptoms and complications associated with higher perimenopause morbidity and a lack of engagement with healthcare professionals. It underscores the need for early and continued education on perimenopause, improved doctor–patient communication, and specific interventions to boost knowledge and attitudes toward perimenopause. Full article

This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R² of 0.60 and a lower MAE of 10.52, compared to the baseline model’s R² of 0.48 and MAE of 16.62. The extended model’s feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers. Full article

The psychedelic renaissance has reignited interest in the therapeutic potential of psychedelics for mental health and well-being. An emerging area of interest is the potential modulation of psychedelic effects by the gut microbiome—the ecosystem of microorganisms in our digestive tract. This review explores the intersection of the gut microbiome and psychedelic therapy, underlining potential implications for personalized medicine and mental health. We delve into the current understanding of the gut–brain axis, its influence on mood, cognition, and behavior, and how the microbiome may affect the metabolism and bioavailability of psychedelic substances. We also discuss the role of microbiome variations in shaping individual responses to psychedelics, along with potential risks and benefits. Moreover, we consider the prospect of microbiome-targeted interventions as a fresh approach to boost or modulate psychedelic therapy’s effectiveness. By integrating insights from the fields of psychopharmacology, microbiology, and neuroscience, our objective is to advance knowledge about the intricate relationship between the microbiome and psychedelic substances, thereby paving the way for novel strategies to optimize mental health outcomes amid the ongoing psychedelic renaissance. Full article

Optimal Reactive Power Dispatch (ORPD) is one of the main challenges in power system operations. ORPD is a non-linear optimization task that aims to reduce the active power losses in the transmission grid, minimize voltage variations, and improve the system voltage stability. This paper proposes an intelligent augmented social network search (ASNS) algorithm for meeting the previous aims compared with the social network search (SNS) algorithm. The social network users’ dialogue, imitation, creativity, and disputation moods drive the core of the SNS algorithm. The proposed ASNS enhances SNS performance by boosting the search capability surrounding the best possible solution, with the goal of improving its globally searched possibilities while attempting to avoid getting locked in a locally optimal one. The performance of ASNS is evaluated compared with SNS on three IEEE standard grids, IEEE 30-, 57-, and 118-bus test systems, for enhanced results. Diverse comparisons and statistical analyses are applied to validate the performance. Results indicated that ASNS supports the diversity of populations in addition to achieving superiority in reducing power losses up to 22% and improving voltage profiles up to 90.3% for the tested power grids. Full article

The reciprocal connectivity between the medial prefrontal cortex (mPFC) and the dorsal raphe nucleus (DR) is involved in mood control and resilience to stress. The infralimbic subdivision (IL) of the mPFC is the rodent equivalent of the ventral anterior cingulate cortex, which is intimately related to the pathophysiology/treatment of major depressive disorder (MDD). Boosting excitatory neurotransmission in the IL—but not in the prelimbic cortex, PrL—evokes depressive-like or antidepressant-like behaviors in rodents, which are associated with changes in serotonergic (5-HT) neurotransmission. We therefore examined the control of 5-HT activity by both of the mPFC subdivisions in anesthetized rats. The electrical stimulation of IL and PrL at 0.9 Hz comparably inhibited 5-HT neurons (53% vs. 48%, respectively). However, stimulation at higher frequencies (10–20 Hz) revealed a greater proportion of 5-HT neurons sensitive to IL than to PrL stimulation (86% vs. 59%, at 20 Hz, respectively), together with a differential involvement of GABA_A (but not 5-HT_1A) receptors. Likewise, electrical and optogenetic stimulation of IL and PrL enhanced 5-HT release in DR in a frequency-dependent manner, with greater elevations after IL stimulation at 20 Hz. Hence, IL and PrL differentially control serotonergic activity, with an apparent superior role of IL, an observation that may help to clarify the brain circuits involved in MDD. Full article

Citrus is one of the main fruit crops cultivated in tropical and subtropical regions worldwide. Approximately half (40–47%) of the fruit mass is inedible and discarded as waste after processing, which causes pollution to the environment. Essential oils (EOs) are aromatic compounds found in significant quantities in oil sacs or oil glands present in the leaves, flowers, and fruit peels (mainly the flavedo part). Citrus EO is a complex mixture of ~400 compounds and has been found to be useful in aromatic infusions for personal health care, perfumes, pharmaceuticals, color enhancers in foods and beverages, and aromatherapy. The citrus EOs possess a pleasant scent, and impart relaxing, calming, mood-uplifting, and cheer-enhancing effects. In aromatherapy, it is applied either in message oils or in diffusion sprays for homes and vehicle sittings. The diffusion creates a fresh feeling and enhances relaxation from stress and anxiety and helps uplifting mood and boosting emotional and physical energy. This review presents a comprehensive outlook on the composition, properties, characterization, and mechanism of action of the citrus EOs in various health-related issues, with a focus on its antioxidant properties. Full article

Using equalization to improve sound listening experience is a well-established topic among the audio society. Finding a general equalization curve is a difficult task because of spectral content influenced by the reproduction system (loudspeakers and room environment) and personal preference diversity. Listeners’ mood is said to be a factor that affects the individual equalization preference. In this study, the effect of a listener’s mood on equalization preference is tried to be investigated. Starting from an experiment with fifty-two listeners, considering five predefined equalization curves and a database of ten music excerpts, the relationship between listeners’ mood and preferred sound equalization has been studied. The main findings of this study showed that the “High-frequency boosting” equalization was the most preferred among participants. However, the “High-frequency boosting” preference of low-aroused people was slightly lower than the high aroused listeners, increasing the preference of the “Low-frequency boosting”. Full article

The evolution of computers in recent years has given a strong boost to research techniques aimed at improving human–machine interaction. These techniques tend to simulate the dynamics of the human–human interaction process, which is based on our innate ability to understand the emotions of other humans. In this work, we present the design of a classifier to recognize the emotions expressed by human beings, and we discuss the results of its testing in a culture-specific case study. The classifier relies exclusively on the gestures people perform, without the need to access additional information, such as facial expressions, the tone of a voice, or the words spoken. The specific purpose is to test whether a computer can correctly recognize emotions starting only from gestures. More generally, it is intended to allow interactive systems to be able to automatically change their behaviour based on the recognized mood, such as adapting the information contents proposed or the flow of interaction, in analogy to what normally happens in the interaction between humans. The document first introduces the operating context, giving an overview of the recognition of emotions and the approach used. Subsequently, the relevant bibliography is described and analysed, highlighting the strengths of the proposed solution. The document continues with a description of the design and implementation of the classifier and of the study we carried out to validate it. The paper ends with a discussion of the results and a short overview of possible implications. Full article